Overview

Dataset statistics

Number of variables31
Number of observations206593
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.5 MiB
Average record size in memory296.9 B

Variable types

Categorical12
Numeric19

Alerts

id has a high cardinality: 206593 distinct valuesHigh cardinality
first_browser has a high cardinality: 52 distinct valuesHigh cardinality
signup_flow is highly overall correlated with signup_appHigh correlation
days_from_first_active_untill_booking is highly overall correlated with days_from_account_created_untill_first_booking and 4 other fieldsHigh correlation
days_from_account_created_untill_first_booking is highly overall correlated with days_from_first_active_untill_booking and 4 other fieldsHigh correlation
year_first_active is highly overall correlated with year_account_createdHigh correlation
month_first_active is highly overall correlated with week_of_year_first_active and 2 other fieldsHigh correlation
day_first_active is highly overall correlated with day_account_createdHigh correlation
day_of_week__first_active is highly overall correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly overall correlated with month_first_active and 2 other fieldsHigh correlation
year_first_booking is highly overall correlated with days_from_first_active_untill_booking and 4 other fieldsHigh correlation
month_first_booking is highly overall correlated with week_of_year_first_bookingHigh correlation
day_first_booking is highly overall correlated with days_from_first_active_untill_booking and 3 other fieldsHigh correlation
day_of_week_first_booking is highly overall correlated with days_from_first_active_untill_booking and 3 other fieldsHigh correlation
week_of_year_first_booking is highly overall correlated with month_first_bookingHigh correlation
month_account_created is highly overall correlated with month_first_active and 2 other fieldsHigh correlation
day_account_created is highly overall correlated with day_first_activeHigh correlation
day_of_week_account_created is highly overall correlated with day_of_week__first_activeHigh correlation
week_of_year_account_created is highly overall correlated with month_first_active and 2 other fieldsHigh correlation
affiliate_channel is highly overall correlated with affiliate_providerHigh correlation
affiliate_provider is highly overall correlated with affiliate_channelHigh correlation
signup_app is highly overall correlated with signup_flow and 2 other fieldsHigh correlation
first_device_type is highly overall correlated with signup_app and 1 other fieldsHigh correlation
first_browser is highly overall correlated with signup_app and 1 other fieldsHigh correlation
year_account_created is highly overall correlated with days_from_first_active_untill_booking and 3 other fieldsHigh correlation
language is highly imbalanced (93.0%)Imbalance
affiliate_provider is highly imbalanced (63.5%)Imbalance
signup_app is highly imbalanced (61.3%)Imbalance
first_browser is highly imbalanced (55.2%)Imbalance
country_destination is highly imbalanced (52.9%)Imbalance
days_from_first_active_untill_account_created is highly skewed (γ1 = 69.29642597)Skewed
id is uniformly distributedUniform
id has unique valuesUnique
signup_flow has 162557 (78.7%) zerosZeros
days_from_first_active_untill_booking has 20738 (10.0%) zerosZeros
days_from_first_active_untill_account_created has 206421 (99.9%) zerosZeros
days_from_account_created_untill_first_booking has 20741 (10.0%) zerosZeros
day_of_week__first_active has 31837 (15.4%) zerosZeros
day_of_week_first_booking has 132217 (64.0%) zerosZeros
day_of_week_account_created has 31830 (15.4%) zerosZeros

Reproduction

Analysis started2023-04-10 02:41:21.295426
Analysis finished2023-04-10 02:42:47.905242
Duration1 minute and 26.61 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct206593
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
gxn3p5htnn
 
1
rioo9za4y8
 
1
oypwb8dpjq
 
1
rfbi1jdtsi
 
1
1n4g6rkyaj
 
1
Other values (206588)
206588 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2065930
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206593 ?
Unique (%)100.0%

Sample

1st rowgxn3p5htnn
2nd row820tgsjxq7
3rd row4ft3gnwmtx
4th rowbjjt8pjhuk
5th row87mebub9p4

Common Values

ValueCountFrequency (%)
gxn3p5htnn 1
 
< 0.1%
rioo9za4y8 1
 
< 0.1%
oypwb8dpjq 1
 
< 0.1%
rfbi1jdtsi 1
 
< 0.1%
1n4g6rkyaj 1
 
< 0.1%
todvt5z6pw 1
 
< 0.1%
1w4hlsh371 1
 
< 0.1%
wq51bf02mm 1
 
< 0.1%
qg0niicmf2 1
 
< 0.1%
iw6a03tix5 1
 
< 0.1%
Other values (206583) 206583
> 99.9%

Length

2023-04-09T19:42:48.003714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gxn3p5htnn 1
 
< 0.1%
yuuqmid2rp 1
 
< 0.1%
cheova4spt 1
 
< 0.1%
hql77nu2lk 1
 
< 0.1%
4ft3gnwmtx 1
 
< 0.1%
bjjt8pjhuk 1
 
< 0.1%
87mebub9p4 1
 
< 0.1%
osr2jwljor 1
 
< 0.1%
lsw9q7uk0j 1
 
< 0.1%
0d01nltbrs 1
 
< 0.1%
Other values (206583) 206583
> 99.9%

Most occurring characters

ValueCountFrequency (%)
t 57855
 
2.8%
h 57719
 
2.8%
y 57712
 
2.8%
o 57682
 
2.8%
f 57593
 
2.8%
4 57586
 
2.8%
1 57560
 
2.8%
b 57558
 
2.8%
j 57550
 
2.8%
i 57525
 
2.8%
Other values (26) 1489590
72.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1492613
72.2%
Decimal Number 573317
 
27.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 57855
 
3.9%
h 57719
 
3.9%
y 57712
 
3.9%
o 57682
 
3.9%
f 57593
 
3.9%
b 57558
 
3.9%
j 57550
 
3.9%
i 57525
 
3.9%
w 57514
 
3.9%
a 57500
 
3.9%
Other values (16) 916405
61.4%
Decimal Number
ValueCountFrequency (%)
4 57586
10.0%
1 57560
10.0%
2 57493
10.0%
7 57460
10.0%
3 57364
10.0%
8 57349
10.0%
9 57336
10.0%
0 57106
10.0%
5 57052
10.0%
6 57011
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1492613
72.2%
Common 573317
 
27.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 57855
 
3.9%
h 57719
 
3.9%
y 57712
 
3.9%
o 57682
 
3.9%
f 57593
 
3.9%
b 57558
 
3.9%
j 57550
 
3.9%
i 57525
 
3.9%
w 57514
 
3.9%
a 57500
 
3.9%
Other values (16) 916405
61.4%
Common
ValueCountFrequency (%)
4 57586
10.0%
1 57560
10.0%
2 57493
10.0%
7 57460
10.0%
3 57364
10.0%
8 57349
10.0%
9 57336
10.0%
0 57106
10.0%
5 57052
10.0%
6 57011
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2065930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 57855
 
2.8%
h 57719
 
2.8%
y 57712
 
2.8%
o 57682
 
2.8%
f 57593
 
2.8%
4 57586
 
2.8%
1 57560
 
2.8%
b 57558
 
2.8%
j 57550
 
2.8%
i 57525
 
2.8%
Other values (26) 1489590
72.1%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
-unknown-
91706 
FEMALE
61520 
MALE
53092 
OTHER
 
275

Length

Max length9
Median length6
Mean length6.8163829
Min length4

Characters and Unicode

Total characters1408217
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-unknown-
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th row-unknown-

Common Values

ValueCountFrequency (%)
-unknown- 91706
44.4%
FEMALE 61520
29.8%
MALE 53092
25.7%
OTHER 275
 
0.1%

Length

2023-04-09T19:42:48.140311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T19:42:48.305654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
unknown 91706
44.4%
female 61520
29.8%
male 53092
25.7%
other 275
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 275118
19.5%
- 183412
13.0%
E 176407
12.5%
M 114612
8.1%
A 114612
8.1%
L 114612
8.1%
u 91706
 
6.5%
k 91706
 
6.5%
o 91706
 
6.5%
w 91706
 
6.5%
Other values (5) 62620
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 641942
45.6%
Uppercase Letter 582863
41.4%
Dash Punctuation 183412
 
13.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 176407
30.3%
M 114612
19.7%
A 114612
19.7%
L 114612
19.7%
F 61520
 
10.6%
O 275
 
< 0.1%
T 275
 
< 0.1%
H 275
 
< 0.1%
R 275
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
n 275118
42.9%
u 91706
 
14.3%
k 91706
 
14.3%
o 91706
 
14.3%
w 91706
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 183412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1224805
87.0%
Common 183412
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 275118
22.5%
E 176407
14.4%
M 114612
9.4%
A 114612
9.4%
L 114612
9.4%
u 91706
 
7.5%
k 91706
 
7.5%
o 91706
 
7.5%
w 91706
 
7.5%
F 61520
 
5.0%
Other values (4) 1100
 
0.1%
Common
ValueCountFrequency (%)
- 183412
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1408217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 275118
19.5%
- 183412
13.0%
E 176407
12.5%
M 114612
8.1%
A 114612
8.1%
L 114612
8.1%
u 91706
 
6.5%
k 91706
 
6.5%
o 91706
 
6.5%
w 91706
 
6.5%
Other values (5) 62620
 
4.4%

age
Real number (ℝ)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.117424
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:48.471427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile24
Q132
median49
Q349
95-th percentile57
Maximum115
Range99
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.156497
Coefficient of variation (CV)0.28863344
Kurtosis4.6261166
Mean42.117424
Median Absolute Deviation (MAD)6
Skewness1.0019742
Sum8701165
Variance147.78042
MonotonicityNot monotonic
2023-04-09T19:42:48.647991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 85257
41.3%
30 6039
 
2.9%
31 5935
 
2.9%
29 5894
 
2.9%
28 5862
 
2.8%
32 5763
 
2.8%
27 5671
 
2.7%
33 5455
 
2.6%
26 4960
 
2.4%
34 4940
 
2.4%
Other values (89) 70817
34.3%
ValueCountFrequency (%)
16 26
 
< 0.1%
17 64
 
< 0.1%
18 665
 
0.3%
19 1097
 
0.5%
20 533
 
0.3%
21 969
 
0.5%
22 1679
 
0.8%
23 2424
1.2%
24 3173
1.5%
25 4405
2.1%
ValueCountFrequency (%)
115 12
 
< 0.1%
113 4
 
< 0.1%
112 1
 
< 0.1%
111 2
 
< 0.1%
110 188
 
0.1%
109 31
 
< 0.1%
108 15
 
< 0.1%
107 23
 
< 0.1%
106 17
 
< 0.1%
105 1127
0.5%

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
basic
147635 
facebook
58412 
google
 
546

Length

Max length8
Median length5
Mean length5.8508614
Min length5

Characters and Unicode

Total characters1208747
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowfacebook
3rd rowbasic
4th rowfacebook
5th rowbasic

Common Values

ValueCountFrequency (%)
basic 147635
71.5%
facebook 58412
 
28.3%
google 546
 
0.3%

Length

2023-04-09T19:42:48.822170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T19:42:48.987256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
basic 147635
71.5%
facebook 58412
 
28.3%
google 546
 
0.3%

Most occurring characters

ValueCountFrequency (%)
b 206047
17.0%
a 206047
17.0%
c 206047
17.0%
s 147635
12.2%
i 147635
12.2%
o 117916
9.8%
e 58958
 
4.9%
f 58412
 
4.8%
k 58412
 
4.8%
g 1092
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1208747
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 206047
17.0%
a 206047
17.0%
c 206047
17.0%
s 147635
12.2%
i 147635
12.2%
o 117916
9.8%
e 58958
 
4.9%
f 58412
 
4.8%
k 58412
 
4.8%
g 1092
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1208747
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 206047
17.0%
a 206047
17.0%
c 206047
17.0%
s 147635
12.2%
i 147635
12.2%
o 117916
9.8%
e 58958
 
4.9%
f 58412
 
4.8%
k 58412
 
4.8%
g 1092
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1208747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 206047
17.0%
a 206047
17.0%
c 206047
17.0%
s 147635
12.2%
i 147635
12.2%
o 117916
9.8%
e 58958
 
4.9%
f 58412
 
4.8%
k 58412
 
4.8%
g 1092
 
0.1%

signup_flow
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1569463
Minimum0
Maximum25
Zeros162557
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:49.124978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.5506836
Coefficient of variation (CV)2.3917682
Kurtosis3.5516157
Mean3.1569463
Median Absolute Deviation (MAD)0
Skewness2.2837836
Sum652203
Variance57.012823
MonotonicityNot monotonic
2023-04-09T19:42:49.262542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 162557
78.7%
25 13724
 
6.6%
12 8897
 
4.3%
3 7550
 
3.7%
2 5522
 
2.7%
24 3975
 
1.9%
23 2793
 
1.4%
1 837
 
0.4%
6 240
 
0.1%
8 237
 
0.1%
Other values (7) 261
 
0.1%
ValueCountFrequency (%)
0 162557
78.7%
1 837
 
0.4%
2 5522
 
2.7%
3 7550
 
3.7%
4 1
 
< 0.1%
5 31
 
< 0.1%
6 240
 
0.1%
8 237
 
0.1%
10 2
 
< 0.1%
12 8897
 
4.3%
ValueCountFrequency (%)
25 13724
6.6%
24 3975
 
1.9%
23 2793
 
1.4%
21 195
 
0.1%
20 14
 
< 0.1%
16 9
 
< 0.1%
15 9
 
< 0.1%
12 8897
4.3%
10 2
 
< 0.1%
8 237
 
0.1%

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
en
199636 
zh
 
1599
fr
 
1146
es
 
888
ko
 
720
Other values (20)
 
2604

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters413186
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en 199636
96.6%
zh 1599
 
0.8%
fr 1146
 
0.6%
es 888
 
0.4%
ko 720
 
0.3%
de 715
 
0.3%
it 489
 
0.2%
ru 378
 
0.2%
pt 234
 
0.1%
ja 224
 
0.1%
Other values (15) 564
 
0.3%

Length

2023-04-09T19:42:49.398638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en 199636
96.6%
zh 1599
 
0.8%
fr 1146
 
0.6%
es 888
 
0.4%
ko 720
 
0.3%
de 715
 
0.3%
it 489
 
0.2%
ru 378
 
0.2%
pt 234
 
0.1%
ja 224
 
0.1%
Other values (15) 564
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 201263
48.7%
n 199760
48.3%
h 1641
 
0.4%
z 1599
 
0.4%
r 1589
 
0.4%
f 1160
 
0.3%
s 1046
 
0.3%
t 809
 
0.2%
d 795
 
0.2%
o 750
 
0.2%
Other values (9) 2774
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 413186
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 201263
48.7%
n 199760
48.3%
h 1641
 
0.4%
z 1599
 
0.4%
r 1589
 
0.4%
f 1160
 
0.3%
s 1046
 
0.3%
t 809
 
0.2%
d 795
 
0.2%
o 750
 
0.2%
Other values (9) 2774
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 413186
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 201263
48.7%
n 199760
48.3%
h 1641
 
0.4%
z 1599
 
0.4%
r 1589
 
0.4%
f 1160
 
0.3%
s 1046
 
0.3%
t 809
 
0.2%
d 795
 
0.2%
o 750
 
0.2%
Other values (9) 2774
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 413186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 201263
48.7%
n 199760
48.3%
h 1641
 
0.4%
z 1599
 
0.4%
r 1589
 
0.4%
f 1160
 
0.3%
s 1046
 
0.3%
t 809
 
0.2%
d 795
 
0.2%
o 750
 
0.2%
Other values (9) 2774
 
0.7%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
direct
133678 
sem-brand
25681 
sem-non-brand
17949 
seo
 
8420
other
 
8296
Other values (3)
 
12569

Length

Max length13
Median length6
Mean length6.7501077
Min length3

Characters and Unicode

Total characters1394525
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowseo
3rd rowdirect
4th rowdirect
5th rowdirect

Common Values

ValueCountFrequency (%)
direct 133678
64.7%
sem-brand 25681
 
12.4%
sem-non-brand 17949
 
8.7%
seo 8420
 
4.1%
other 8296
 
4.0%
api 7736
 
3.7%
content 3780
 
1.8%
remarketing 1053
 
0.5%

Length

2023-04-09T19:42:49.532241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T19:42:49.713781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
direct 133678
64.7%
sem-brand 25681
 
12.4%
sem-non-brand 17949
 
8.7%
seo 8420
 
4.1%
other 8296
 
4.0%
api 7736
 
3.7%
content 3780
 
1.8%
remarketing 1053
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 199910
14.3%
r 187710
13.5%
d 177308
12.7%
t 150587
10.8%
i 142467
10.2%
c 137458
9.9%
n 88141
6.3%
- 61579
 
4.4%
a 52419
 
3.8%
s 52050
 
3.7%
Other values (7) 144896
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1332946
95.6%
Dash Punctuation 61579
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 199910
15.0%
r 187710
14.1%
d 177308
13.3%
t 150587
11.3%
i 142467
10.7%
c 137458
10.3%
n 88141
6.6%
a 52419
 
3.9%
s 52050
 
3.9%
m 44683
 
3.4%
Other values (6) 100213
7.5%
Dash Punctuation
ValueCountFrequency (%)
- 61579
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1332946
95.6%
Common 61579
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 199910
15.0%
r 187710
14.1%
d 177308
13.3%
t 150587
11.3%
i 142467
10.7%
c 137458
10.3%
n 88141
6.6%
a 52419
 
3.9%
s 52050
 
3.9%
m 44683
 
3.4%
Other values (6) 100213
7.5%
Common
ValueCountFrequency (%)
- 61579
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1394525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 199910
14.3%
r 187710
13.5%
d 177308
12.7%
t 150587
10.8%
i 142467
10.2%
c 137458
9.9%
n 88141
6.3%
- 61579
 
4.4%
a 52419
 
3.8%
s 52050
 
3.7%
Other values (7) 144896
10.4%

affiliate_provider
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
direct
133438 
google
50252 
other
 
11867
craigslist
 
2964
bing
 
2253
Other values (13)
 
5819

Length

Max length19
Median length6
Mean length6.0352335
Min length3

Characters and Unicode

Total characters1246837
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowdirect
2nd rowgoogle
3rd rowdirect
4th rowdirect
5th rowdirect

Common Values

ValueCountFrequency (%)
direct 133438
64.6%
google 50252
 
24.3%
other 11867
 
5.7%
craigslist 2964
 
1.4%
bing 2253
 
1.1%
facebook 2196
 
1.1%
padmapper 766
 
0.4%
vast 748
 
0.4%
facebook-open-graph 545
 
0.3%
yahoo 495
 
0.2%
Other values (8) 1069
 
0.5%

Length

2023-04-09T19:42:49.891098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct 133438
64.6%
google 50252
 
24.3%
other 11867
 
5.7%
craigslist 2964
 
1.4%
bing 2253
 
1.1%
facebook 2196
 
1.1%
padmapper 766
 
0.4%
vast 748
 
0.4%
facebook-open-graph 545
 
0.3%
yahoo 495
 
0.2%
Other values (8) 1069
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 200698
16.1%
r 149795
12.0%
t 149527
12.0%
i 141974
11.4%
c 139143
11.2%
d 134251
10.8%
o 119388
9.6%
g 106881
8.6%
l 53379
 
4.3%
h 12907
 
1.0%
Other values (14) 38894
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1245584
99.9%
Dash Punctuation 1253
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 200698
16.1%
r 149795
12.0%
t 149527
12.0%
i 141974
11.4%
c 139143
11.2%
d 134251
10.8%
o 119388
9.6%
g 106881
8.6%
l 53379
 
4.3%
h 12907
 
1.0%
Other values (13) 37641
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 1253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1245584
99.9%
Common 1253
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 200698
16.1%
r 149795
12.0%
t 149527
12.0%
i 141974
11.4%
c 139143
11.2%
d 134251
10.8%
o 119388
9.6%
g 106881
8.6%
l 53379
 
4.3%
h 12907
 
1.0%
Other values (13) 37641
 
3.0%
Common
ValueCountFrequency (%)
- 1253
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1246837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 200698
16.1%
r 149795
12.0%
t 149527
12.0%
i 141974
11.4%
c 139143
11.2%
d 134251
10.8%
o 119388
9.6%
g 106881
8.6%
l 53379
 
4.3%
h 12907
 
1.0%
Other values (14) 38894
 
3.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
untracked
108838 
linked
46084 
omg
43830 
tracked-other
 
6123
product
 
1545
Other values (2)
 
173

Length

Max length13
Median length9
Mean length7.1614576
Min length3

Characters and Unicode

Total characters1479507
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowuntracked

Common Values

ValueCountFrequency (%)
untracked 108838
52.7%
linked 46084
22.3%
omg 43830
21.2%
tracked-other 6123
 
3.0%
product 1545
 
0.7%
marketing 139
 
0.1%
local ops 34
 
< 0.1%

Length

2023-04-09T19:42:50.042034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T19:42:50.211255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
untracked 108838
52.7%
linked 46084
22.3%
omg 43830
21.2%
tracked-other 6123
 
3.0%
product 1545
 
0.7%
marketing 139
 
0.1%
local 34
 
< 0.1%
ops 34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 167307
11.3%
d 162590
11.0%
k 161184
10.9%
n 155061
10.5%
t 122768
8.3%
r 122768
8.3%
c 116540
7.9%
a 115134
7.8%
u 110383
7.5%
o 51566
 
3.5%
Other values (9) 194206
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1473350
99.6%
Dash Punctuation 6123
 
0.4%
Space Separator 34
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 167307
11.4%
d 162590
11.0%
k 161184
10.9%
n 155061
10.5%
t 122768
8.3%
r 122768
8.3%
c 116540
7.9%
a 115134
7.8%
u 110383
7.5%
o 51566
 
3.5%
Other values (7) 188049
12.8%
Dash Punctuation
ValueCountFrequency (%)
- 6123
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1473350
99.6%
Common 6157
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 167307
11.4%
d 162590
11.0%
k 161184
10.9%
n 155061
10.5%
t 122768
8.3%
r 122768
8.3%
c 116540
7.9%
a 115134
7.8%
u 110383
7.5%
o 51566
 
3.5%
Other values (7) 188049
12.8%
Common
ValueCountFrequency (%)
- 6123
99.4%
34
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1479507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 167307
11.3%
d 162590
11.0%
k 161184
10.9%
n 155061
10.5%
t 122768
8.3%
r 122768
8.3%
c 116540
7.9%
a 115134
7.8%
u 110383
7.5%
o 51566
 
3.5%
Other values (9) 194206
13.1%

signup_app
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Web
177591 
iOS
17852 
Moweb
 
5771
Android
 
5379

Length

Max length7
Median length3
Mean length3.1600151
Min length3

Characters and Unicode

Total characters652837
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web 177591
86.0%
iOS 17852
 
8.6%
Moweb 5771
 
2.8%
Android 5379
 
2.6%

Length

2023-04-09T19:42:50.370178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T19:42:50.542060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
web 177591
86.0%
ios 17852
 
8.6%
moweb 5771
 
2.8%
android 5379
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 183362
28.1%
b 183362
28.1%
W 177591
27.2%
i 23231
 
3.6%
O 17852
 
2.7%
S 17852
 
2.7%
o 11150
 
1.7%
d 10758
 
1.6%
M 5771
 
0.9%
w 5771
 
0.9%
Other values (3) 16137
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 428392
65.6%
Uppercase Letter 224445
34.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 183362
42.8%
b 183362
42.8%
i 23231
 
5.4%
o 11150
 
2.6%
d 10758
 
2.5%
w 5771
 
1.3%
n 5379
 
1.3%
r 5379
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
W 177591
79.1%
O 17852
 
8.0%
S 17852
 
8.0%
M 5771
 
2.6%
A 5379
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 652837
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 183362
28.1%
b 183362
28.1%
W 177591
27.2%
i 23231
 
3.6%
O 17852
 
2.7%
S 17852
 
2.7%
o 11150
 
1.7%
d 10758
 
1.6%
M 5771
 
0.9%
w 5771
 
0.9%
Other values (3) 16137
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 652837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 183362
28.1%
b 183362
28.1%
W 177591
27.2%
i 23231
 
3.6%
O 17852
 
2.7%
S 17852
 
2.7%
o 11150
 
1.7%
d 10758
 
1.6%
M 5771
 
0.9%
w 5771
 
0.9%
Other values (3) 16137
 
2.5%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Mac Desktop
89255 
Windows Desktop
72410 
iPhone
20712 
iPad
14281 
Other/Unknown
 
4591
Other values (4)
 
5344

Length

Max length18
Median length15
Mean length11.532617
Min length4

Characters and Unicode

Total characters2382558
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowMac Desktop
3rd rowWindows Desktop
4th rowMac Desktop
5th rowMac Desktop

Common Values

ValueCountFrequency (%)
Mac Desktop 89255
43.2%
Windows Desktop 72410
35.0%
iPhone 20712
 
10.0%
iPad 14281
 
6.9%
Other/Unknown 4591
 
2.2%
Android Phone 2788
 
1.3%
Android Tablet 1285
 
0.6%
Desktop (Other) 1196
 
0.6%
SmartPhone (Other) 75
 
< 0.1%

Length

2023-04-09T19:42:50.679057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T19:42:50.862266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
desktop 162861
43.6%
mac 89255
23.9%
windows 72410
19.4%
iphone 20712
 
5.5%
ipad 14281
 
3.8%
other/unknown 4591
 
1.2%
android 4073
 
1.1%
phone 2788
 
0.7%
tablet 1285
 
0.3%
other 1271
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 267510
11.2%
s 235271
 
9.9%
e 193583
 
8.1%
t 170083
 
7.1%
k 167452
 
7.0%
167009
 
7.0%
D 162861
 
6.8%
p 162861
 
6.8%
n 113831
 
4.8%
i 111476
 
4.7%
Other values (20) 630621
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1830148
76.8%
Uppercase Letter 378268
 
15.9%
Space Separator 167009
 
7.0%
Other Punctuation 4591
 
0.2%
Open Punctuation 1271
 
0.1%
Close Punctuation 1271
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 267510
14.6%
s 235271
12.9%
e 193583
10.6%
t 170083
9.3%
k 167452
9.1%
p 162861
8.9%
n 113831
6.2%
i 111476
6.1%
a 104896
 
5.7%
d 94837
 
5.2%
Other values (7) 208348
11.4%
Uppercase Letter
ValueCountFrequency (%)
D 162861
43.1%
M 89255
23.6%
W 72410
19.1%
P 37856
 
10.0%
O 5862
 
1.5%
U 4591
 
1.2%
A 4073
 
1.1%
T 1285
 
0.3%
S 75
 
< 0.1%
Space Separator
ValueCountFrequency (%)
167009
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4591
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1271
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2208416
92.7%
Common 174142
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 267510
12.1%
s 235271
10.7%
e 193583
 
8.8%
t 170083
 
7.7%
k 167452
 
7.6%
D 162861
 
7.4%
p 162861
 
7.4%
n 113831
 
5.2%
i 111476
 
5.0%
a 104896
 
4.7%
Other values (16) 518592
23.5%
Common
ValueCountFrequency (%)
167009
95.9%
/ 4591
 
2.6%
( 1271
 
0.7%
) 1271
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2382558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 267510
11.2%
s 235271
 
9.9%
e 193583
 
8.1%
t 170083
 
7.1%
k 167452
 
7.0%
167009
 
7.0%
D 162861
 
6.8%
p 162861
 
6.8%
n 113831
 
4.8%
i 111476
 
4.7%
Other values (20) 630621
26.5%

first_browser
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE 

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Chrome
63620 
Safari
44981 
Firefox
33513 
-unknown-
21166 
IE
20970 
Other values (47)
22343 

Length

Max length20
Median length6
Mean length6.8085027
Min length2

Characters and Unicode

Total characters1406589
Distinct characters50
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowChrome
3rd rowIE
4th rowFirefox
5th rowChrome

Common Values

ValueCountFrequency (%)
Chrome 63620
30.8%
Safari 44981
21.8%
Firefox 33513
16.2%
-unknown- 21166
 
10.2%
IE 20970
 
10.2%
Mobile Safari 19195
 
9.3%
Chrome Mobile 1258
 
0.6%
Android Browser 844
 
0.4%
AOL Explorer 240
 
0.1%
Opera 187
 
0.1%
Other values (42) 619
 
0.3%

Length

2023-04-09T19:42:51.047265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome 64878
28.4%
safari 64176
28.1%
firefox 33543
14.7%
unknown 21166
 
9.3%
ie 21006
 
9.2%
mobile 20521
 
9.0%
browser 907
 
0.4%
android 844
 
0.4%
explorer 273
 
0.1%
aol 240
 
0.1%
Other values (48) 799
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 166259
11.8%
o 142522
 
10.1%
a 128752
 
9.2%
e 120588
 
8.6%
i 119405
 
8.5%
f 97719
 
6.9%
m 65051
 
4.6%
h 65000
 
4.6%
C 64982
 
4.6%
n 64472
 
4.6%
Other values (40) 371839
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1113623
79.2%
Uppercase Letter 228859
 
16.3%
Dash Punctuation 42332
 
3.0%
Space Separator 21760
 
1.5%
Other Punctuation 11
 
< 0.1%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 166259
14.9%
o 142522
12.8%
a 128752
11.6%
e 120588
10.8%
i 119405
10.7%
f 97719
8.8%
m 65051
 
5.8%
h 65000
 
5.8%
n 64472
 
5.8%
x 33881
 
3.0%
Other values (14) 109974
9.9%
Uppercase Letter
ValueCountFrequency (%)
C 64982
28.4%
S 64378
28.1%
F 33561
14.7%
E 21281
 
9.3%
I 21037
 
9.2%
M 20657
 
9.0%
A 1125
 
0.5%
B 1039
 
0.5%
O 442
 
0.2%
L 240
 
0.1%
Other values (10) 117
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
2 1
25.0%
7 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 42332
100.0%
Space Separator
ValueCountFrequency (%)
21760
100.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1342482
95.4%
Common 64107
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 166259
12.4%
o 142522
10.6%
a 128752
9.6%
e 120588
 
9.0%
i 119405
 
8.9%
f 97719
 
7.3%
m 65051
 
4.8%
h 65000
 
4.8%
C 64982
 
4.8%
n 64472
 
4.8%
Other values (34) 307732
22.9%
Common
ValueCountFrequency (%)
- 42332
66.0%
21760
33.9%
. 11
 
< 0.1%
0 2
 
< 0.1%
2 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1406589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 166259
11.8%
o 142522
 
10.1%
a 128752
 
9.2%
e 120588
 
8.6%
i 119405
 
8.5%
f 97719
 
6.9%
m 65051
 
4.6%
h 65000
 
4.6%
C 64982
 
4.6%
n 64472
 
4.6%
Other values (40) 371839
26.4%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
NDF
119810 
US
60800 
other
 
9935
FR
 
4881
IT
 
2776
Other values (7)
 
8391

Length

Max length5
Median length3
Mean length2.7242017
Min length2

Characters and Unicode

Total characters562801
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowNDF
3rd rowUS
4th rowother
5th rowUS

Common Values

ValueCountFrequency (%)
NDF 119810
58.0%
US 60800
29.4%
other 9935
 
4.8%
FR 4881
 
2.4%
IT 2776
 
1.3%
GB 2285
 
1.1%
ES 2203
 
1.1%
CA 1385
 
0.7%
DE 1033
 
0.5%
NL 746
 
0.4%
Other values (2) 739
 
0.4%

Length

2023-04-09T19:42:51.203143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf 119810
58.0%
us 60800
29.4%
other 9935
 
4.8%
fr 4881
 
2.4%
it 2776
 
1.3%
gb 2285
 
1.1%
es 2203
 
1.1%
ca 1385
 
0.7%
de 1033
 
0.5%
nl 746
 
0.4%
Other values (2) 739
 
0.4%

Most occurring characters

ValueCountFrequency (%)
F 124691
22.2%
D 120843
21.5%
N 120556
21.4%
S 63003
11.2%
U 61326
10.9%
o 9935
 
1.8%
t 9935
 
1.8%
h 9935
 
1.8%
e 9935
 
1.8%
r 9935
 
1.8%
Other values (10) 22707
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 513126
91.2%
Lowercase Letter 49675
 
8.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 124691
24.3%
D 120843
23.6%
N 120556
23.5%
S 63003
12.3%
U 61326
12.0%
R 4881
 
1.0%
E 3236
 
0.6%
T 2989
 
0.6%
I 2776
 
0.5%
G 2285
 
0.4%
Other values (5) 6540
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
o 9935
20.0%
t 9935
20.0%
h 9935
20.0%
e 9935
20.0%
r 9935
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 562801
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 124691
22.2%
D 120843
21.5%
N 120556
21.4%
S 63003
11.2%
U 61326
10.9%
o 9935
 
1.8%
t 9935
 
1.8%
h 9935
 
1.8%
e 9935
 
1.8%
r 9935
 
1.8%
Other values (10) 22707
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 562801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 124691
22.2%
D 120843
21.5%
N 120556
21.4%
S 63003
11.2%
U 61326
10.9%
o 9935
 
1.8%
t 9935
 
1.8%
h 9935
 
1.8%
e 9935
 
1.8%
r 9935
 
1.8%
Other values (10) 22707
 
4.0%

days_from_first_active_untill_booking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1940
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.73604
Minimum0
Maximum2293
Zeros20738
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:51.363496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median416
Q3678
95-th percentile1108
Maximum2293
Range2293
Interquartile range (IQR)672

Descriptive statistics

Standard deviation388.22993
Coefficient of variation (CV)0.92055194
Kurtosis-0.22795045
Mean421.73604
Median Absolute Deviation (MAD)374
Skewness0.62869493
Sum87127714
Variance150722.48
MonotonicityNot monotonic
2023-04-09T19:42:51.557466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20738
 
10.0%
1 14288
 
6.9%
2 6307
 
3.1%
3 3894
 
1.9%
4 2845
 
1.4%
5 2193
 
1.1%
6 1735
 
0.8%
7 1611
 
0.8%
8 1275
 
0.6%
9 1024
 
0.5%
Other values (1930) 150683
72.9%
ValueCountFrequency (%)
0 20738
10.0%
1 14288
6.9%
2 6307
 
3.1%
3 3894
 
1.9%
4 2845
 
1.4%
5 2193
 
1.1%
6 1735
 
0.8%
7 1611
 
0.8%
8 1275
 
0.6%
9 1024
 
0.5%
ValueCountFrequency (%)
2293 1
 
< 0.1%
2228 1
 
< 0.1%
2001 2
< 0.1%
1999 1
 
< 0.1%
1995 2
< 0.1%
1994 1
 
< 0.1%
1992 1
 
< 0.1%
1991 3
< 0.1%
1990 2
< 0.1%
1982 1
 
< 0.1%

days_from_first_active_untill_account_created
Real number (ℝ)

SKEWED  ZEROS 

Distinct142
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23668275
Minimum0
Maximum1456
Zeros206421
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:51.723932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.11225
Coefficient of variation (CV)51.175041
Kurtosis5699.5656
Mean0.23668275
Median Absolute Deviation (MAD)0
Skewness69.296426
Sum48897
Variance146.70659
MonotonicityNot monotonic
2023-04-09T19:42:51.892400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 206421
99.9%
1 6
 
< 0.1%
6 4
 
< 0.1%
5 3
 
< 0.1%
29 3
 
< 0.1%
2 3
 
< 0.1%
7 3
 
< 0.1%
3 3
 
< 0.1%
103 2
 
< 0.1%
95 2
 
< 0.1%
Other values (132) 143
 
0.1%
ValueCountFrequency (%)
0 206421
99.9%
1 6
 
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 4
 
< 0.1%
7 3
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
1456 1
< 0.1%
1369 1
< 0.1%
1361 1
< 0.1%
1148 1
< 0.1%
1036 1
< 0.1%
1018 1
< 0.1%
1011 1
< 0.1%
998 1
< 0.1%
995 1
< 0.1%
882 1
< 0.1%

days_from_account_created_untill_first_booking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1963
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.49936
Minimum-349
Maximum2001
Zeros20741
Zeros (%)10.0%
Negative28
Negative (%)< 0.1%
Memory size3.2 MiB
2023-04-09T19:42:52.071593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-349
5-th percentile0
Q16
median416
Q3678
95-th percentile1108
Maximum2001
Range2350
Interquartile range (IQR)672

Descriptive statistics

Standard deviation388.11799
Coefficient of variation (CV)0.92080328
Kurtosis-0.23235143
Mean421.49936
Median Absolute Deviation (MAD)374
Skewness0.62763033
Sum87078817
Variance150635.58
MonotonicityNot monotonic
2023-04-09T19:42:52.253895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20741
 
10.0%
1 14289
 
6.9%
2 6309
 
3.1%
3 3897
 
1.9%
4 2845
 
1.4%
5 2197
 
1.1%
6 1738
 
0.8%
7 1611
 
0.8%
8 1276
 
0.6%
9 1022
 
0.5%
Other values (1953) 150668
72.9%
ValueCountFrequency (%)
-349 1
< 0.1%
-347 1
< 0.1%
-338 1
< 0.1%
-308 1
< 0.1%
-298 1
< 0.1%
-295 1
< 0.1%
-269 1
< 0.1%
-261 1
< 0.1%
-208 1
< 0.1%
-167 1
< 0.1%
ValueCountFrequency (%)
2001 2
< 0.1%
1999 1
 
< 0.1%
1995 2
< 0.1%
1994 1
 
< 0.1%
1992 1
 
< 0.1%
1991 3
< 0.1%
1990 2
< 0.1%
1982 1
 
< 0.1%
1980 1
 
< 0.1%
1979 1
 
< 0.1%

year_first_active
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.0627
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:52.387242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12013
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90115082
Coefficient of variation (CV)0.00044765164
Kurtosis0.305154
Mean2013.0627
Median Absolute Deviation (MAD)1
Skewness-0.80843674
Sum4.1588466 × 108
Variance0.81207281
MonotonicityIncreasing
2023-04-09T19:42:52.530394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013 81841
39.6%
2014 75496
36.5%
2012 37950
18.4%
2011 9331
 
4.5%
2010 1970
 
1.0%
2009 5
 
< 0.1%
ValueCountFrequency (%)
2009 5
 
< 0.1%
2010 1970
 
1.0%
2011 9331
 
4.5%
2012 37950
18.4%
2013 81841
39.6%
2014 75496
36.5%
ValueCountFrequency (%)
2014 75496
36.5%
2013 81841
39.6%
2012 37950
18.4%
2011 9331
 
4.5%
2010 1970
 
1.0%
2009 5
 
< 0.1%

month_first_active
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.016956
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:52.688018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2214541
Coefficient of variation (CV)0.53539599
Kurtosis-0.95288265
Mean6.016956
Median Absolute Deviation (MAD)3
Skewness0.2529472
Sum1243061
Variance10.377767
MonotonicityNot monotonic
2023-04-09T19:42:52.840567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 27033
13.1%
5 25525
12.4%
4 21333
10.3%
3 19482
9.4%
1 16768
8.1%
2 15853
7.7%
9 14774
7.2%
8 14061
6.8%
7 13410
6.5%
10 13031
6.3%
Other values (2) 25323
12.3%
ValueCountFrequency (%)
1 16768
8.1%
2 15853
7.7%
3 19482
9.4%
4 21333
10.3%
5 25525
12.4%
6 27033
13.1%
7 13410
6.5%
8 14061
6.8%
9 14774
7.2%
10 13031
6.3%
ValueCountFrequency (%)
12 12799
6.2%
11 12524
6.1%
10 13031
6.3%
9 14774
7.2%
8 14061
6.8%
7 13410
6.5%
6 27033
13.1%
5 25525
12.4%
4 21333
10.3%
3 19482
9.4%

day_first_active
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.872677
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:53.018474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7420428
Coefficient of variation (CV)0.55076045
Kurtosis-1.1874752
Mean15.872677
Median Absolute Deviation (MAD)8
Skewness-0.011222017
Sum3279184
Variance76.423312
MonotonicityNot monotonic
2023-04-09T19:42:53.191516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24 7182
 
3.5%
20 7014
 
3.4%
18 7010
 
3.4%
16 6988
 
3.4%
23 6988
 
3.4%
19 6956
 
3.4%
28 6915
 
3.3%
17 6908
 
3.3%
26 6908
 
3.3%
13 6889
 
3.3%
Other values (21) 136835
66.2%
ValueCountFrequency (%)
1 5967
2.9%
2 6561
3.2%
3 6750
3.3%
4 6620
3.2%
5 6817
3.3%
6 6772
3.3%
7 6510
3.2%
8 6698
3.2%
9 6717
3.3%
10 6810
3.3%
ValueCountFrequency (%)
31 3607
1.7%
30 6587
3.2%
29 6363
3.1%
28 6915
3.3%
27 6842
3.3%
26 6908
3.3%
25 6728
3.3%
24 7182
3.5%
23 6988
3.4%
22 6720
3.3%

day_of_week__first_active
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7620539
Minimum0
Maximum6
Zeros31837
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:53.367554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9442686
Coefficient of variation (CV)0.70392132
Kurtosis-1.1497729
Mean2.7620539
Median Absolute Deviation (MAD)2
Skewness0.1677711
Sum570621
Variance3.7801805
MonotonicityNot monotonic
2023-04-09T19:42:53.500232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 33988
16.5%
2 33041
16.0%
0 31837
15.4%
3 31504
15.2%
4 28807
13.9%
6 23731
11.5%
5 23685
11.5%
ValueCountFrequency (%)
0 31837
15.4%
1 33988
16.5%
2 33041
16.0%
3 31504
15.2%
4 28807
13.9%
5 23685
11.5%
6 23731
11.5%
ValueCountFrequency (%)
6 23731
11.5%
5 23685
11.5%
4 28807
13.9%
3 31504
15.2%
2 33041
16.0%
1 33988
16.5%
0 31837
15.4%

week_of_year_first_active
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.37389
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:53.670437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.95401
Coefficient of variation (CV)0.57249827
Kurtosis-0.94114835
Mean24.37389
Median Absolute Deviation (MAD)11
Skewness0.25341813
Sum5035475
Variance194.71439
MonotonicityNot monotonic
2023-04-09T19:42:53.865794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 6779
 
3.3%
25 6426
 
3.1%
24 6164
 
3.0%
21 6116
 
3.0%
23 6062
 
2.9%
20 6057
 
2.9%
22 5602
 
2.7%
19 5490
 
2.7%
18 5433
 
2.6%
17 5309
 
2.6%
Other values (43) 147155
71.2%
ValueCountFrequency (%)
1 3196
1.5%
2 3824
1.9%
3 4026
1.9%
4 3785
1.8%
5 3794
1.8%
6 3913
1.9%
7 3842
1.9%
8 4038
2.0%
9 4281
2.1%
10 4236
2.1%
ValueCountFrequency (%)
53 3
 
< 0.1%
52 2671
1.3%
51 2784
1.3%
50 2890
1.4%
49 3170
1.5%
48 2807
1.4%
47 2890
1.4%
46 3068
1.5%
45 3066
1.5%
44 2745
1.3%

year_first_booking
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.1956
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:54.017145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2012
Q12013
median2015
Q32015
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1387295
Coefficient of variation (CV)0.000565352
Kurtosis0.78253442
Mean2014.1956
Median Absolute Deviation (MAD)0
Skewness-1.2854707
Sum4.161187 × 108
Variance1.2967049
MonotonicityNot monotonic
2023-04-09T19:42:54.146281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2015 121581
58.9%
2014 32334
 
15.7%
2013 31083
 
15.0%
2012 15797
 
7.6%
2011 4690
 
2.3%
2010 1108
 
0.5%
ValueCountFrequency (%)
2010 1108
 
0.5%
2011 4690
 
2.3%
2012 15797
 
7.6%
2013 31083
 
15.0%
2014 32334
 
15.7%
2015 121581
58.9%
ValueCountFrequency (%)
2015 121581
58.9%
2014 32334
 
15.7%
2013 31083
 
15.0%
2012 15797
 
7.6%
2011 4690
 
2.3%
2010 1108
 
0.5%

month_first_booking
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.043593
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:54.293538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median6
Q36
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0591816
Coefficient of variation (CV)0.34072143
Kurtosis1.87404
Mean6.043593
Median Absolute Deviation (MAD)0
Skewness0.38550344
Sum1248564
Variance4.2402291
MonotonicityNot monotonic
2023-04-09T19:42:54.432712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 130148
63.0%
5 10322
 
5.0%
4 8624
 
4.2%
3 8159
 
3.9%
7 7073
 
3.4%
8 6854
 
3.3%
2 6616
 
3.2%
9 6402
 
3.1%
1 6338
 
3.1%
10 6009
 
2.9%
Other values (2) 10048
 
4.9%
ValueCountFrequency (%)
1 6338
 
3.1%
2 6616
 
3.2%
3 8159
 
3.9%
4 8624
 
4.2%
5 10322
 
5.0%
6 130148
63.0%
7 7073
 
3.4%
8 6854
 
3.3%
9 6402
 
3.1%
10 6009
 
2.9%
ValueCountFrequency (%)
12 4944
 
2.4%
11 5104
 
2.5%
10 6009
 
2.9%
9 6402
 
3.1%
8 6854
 
3.3%
7 7073
 
3.4%
6 130148
63.0%
5 10322
 
5.0%
4 8624
 
4.2%
3 8159
 
3.9%

day_first_booking
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.392312
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:54.593500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118
median29
Q329
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.6825814
Coefficient of variation (CV)0.37117243
Kurtosis0.13553141
Mean23.392312
Median Absolute Deviation (MAD)0
Skewness-1.2661289
Sum4832688
Variance75.38722
MonotonicityNot monotonic
2023-04-09T19:42:54.740064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
29 122367
59.2%
10 3009
 
1.5%
17 2996
 
1.5%
11 2982
 
1.4%
16 2964
 
1.4%
13 2950
 
1.4%
15 2950
 
1.4%
5 2918
 
1.4%
12 2892
 
1.4%
3 2882
 
1.4%
Other values (21) 57683
27.9%
ValueCountFrequency (%)
1 2690
1.3%
2 2807
1.4%
3 2882
1.4%
4 2784
1.3%
5 2918
1.4%
6 2876
1.4%
7 2863
1.4%
8 2882
1.4%
9 2848
1.4%
10 3009
1.5%
ValueCountFrequency (%)
31 1526
 
0.7%
30 2650
 
1.3%
29 122367
59.2%
28 2809
 
1.4%
27 2698
 
1.3%
26 2757
 
1.3%
25 2805
 
1.4%
24 2824
 
1.4%
23 2793
 
1.4%
22 2860
 
1.4%

day_of_week_first_booking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1775907
Minimum0
Maximum6
Zeros132217
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:54.889027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8587813
Coefficient of variation (CV)1.5784612
Kurtosis0.48607844
Mean1.1775907
Median Absolute Deviation (MAD)0
Skewness1.3647587
Sum243282
Variance3.4550678
MonotonicityNot monotonic
2023-04-09T19:42:55.022818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 132217
64.0%
2 14029
 
6.8%
1 13970
 
6.8%
3 13627
 
6.6%
4 12972
 
6.3%
5 10183
 
4.9%
6 9595
 
4.6%
ValueCountFrequency (%)
0 132217
64.0%
1 13970
 
6.8%
2 14029
 
6.8%
3 13627
 
6.6%
4 12972
 
6.3%
5 10183
 
4.9%
6 9595
 
4.6%
ValueCountFrequency (%)
6 9595
 
4.6%
5 10183
 
4.9%
4 12972
 
6.3%
3 13627
 
6.6%
2 14029
 
6.8%
1 13970
 
6.8%
0 132217
64.0%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.046705
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:55.217433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q127
median27
Q327
95-th percentile44
Maximum53
Range52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.9866041
Coefficient of variation (CV)0.34501884
Kurtosis1.6636917
Mean26.046705
Median Absolute Deviation (MAD)0
Skewness-0.13773093
Sum5381067
Variance80.759053
MonotonicityNot monotonic
2023-04-09T19:42:55.437718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 121629
58.9%
26 2488
 
1.2%
24 2462
 
1.2%
21 2451
 
1.2%
20 2434
 
1.2%
25 2403
 
1.2%
23 2366
 
1.1%
18 2277
 
1.1%
19 2263
 
1.1%
22 2218
 
1.1%
Other values (43) 63602
30.8%
ValueCountFrequency (%)
1 1092
0.5%
2 1438
0.7%
3 1717
0.8%
4 1401
0.7%
5 1444
0.7%
6 1590
0.8%
7 1719
0.8%
8 1672
0.8%
9 1748
0.8%
10 1840
0.9%
ValueCountFrequency (%)
53 1
 
< 0.1%
52 915
0.4%
51 1130
0.5%
50 1172
0.6%
49 1249
0.6%
48 1121
0.5%
47 1118
0.5%
46 1191
0.6%
45 1354
0.7%
44 1188
0.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2013
81851 
2014
75532 
2012
37936 
2011
9313 
2010
 
1961

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters826372
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2011
3rd row2010
4th row2011
5th row2010

Common Values

ValueCountFrequency (%)
2013 81851
39.6%
2014 75532
36.6%
2012 37936
18.4%
2011 9313
 
4.5%
2010 1961
 
0.9%

Length

2023-04-09T19:42:55.606506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T19:42:55.769052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2013 81851
39.6%
2014 75532
36.6%
2012 37936
18.4%
2011 9313
 
4.5%
2010 1961
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 244529
29.6%
1 215906
26.1%
0 208554
25.2%
3 81851
 
9.9%
4 75532
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 826372
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 244529
29.6%
1 215906
26.1%
0 208554
25.2%
3 81851
 
9.9%
4 75532
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 826372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 244529
29.6%
1 215906
26.1%
0 208554
25.2%
3 81851
 
9.9%
4 75532
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 826372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 244529
29.6%
1 215906
26.1%
0 208554
25.2%
3 81851
 
9.9%
4 75532
 
9.1%

month_account_created
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0169948
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:55.935030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2216538
Coefficient of variation (CV)0.53542572
Kurtosis-0.95303482
Mean6.0169948
Median Absolute Deviation (MAD)3
Skewness0.2528859
Sum1243069
Variance10.379053
MonotonicityNot monotonic
2023-04-09T19:42:56.087302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 27028
13.1%
5 25531
12.4%
4 21324
10.3%
3 19481
9.4%
1 16773
8.1%
2 15855
7.7%
9 14780
7.2%
8 14060
6.8%
7 13405
6.5%
10 13030
6.3%
Other values (2) 25326
12.3%
ValueCountFrequency (%)
1 16773
8.1%
2 15855
7.7%
3 19481
9.4%
4 21324
10.3%
5 25531
12.4%
6 27028
13.1%
7 13405
6.5%
8 14060
6.8%
9 14780
7.2%
10 13030
6.3%
ValueCountFrequency (%)
12 12803
6.2%
11 12523
6.1%
10 13030
6.3%
9 14780
7.2%
8 14060
6.8%
7 13405
6.5%
6 27028
13.1%
5 25531
12.4%
4 21324
10.3%
3 19481
9.4%

day_account_created
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.872953
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:56.260985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7425077
Coefficient of variation (CV)0.55078017
Kurtosis-1.187561
Mean15.872953
Median Absolute Deviation (MAD)8
Skewness-0.011387796
Sum3279241
Variance76.431442
MonotonicityNot monotonic
2023-04-09T19:42:56.410833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24 7183
 
3.5%
20 7018
 
3.4%
18 7009
 
3.4%
16 6994
 
3.4%
23 6990
 
3.4%
19 6955
 
3.4%
28 6921
 
3.4%
17 6904
 
3.3%
26 6904
 
3.3%
13 6883
 
3.3%
Other values (21) 136832
66.2%
ValueCountFrequency (%)
1 5969
2.9%
2 6564
3.2%
3 6754
3.3%
4 6620
3.2%
5 6817
3.3%
6 6770
3.3%
7 6506
3.1%
8 6701
3.2%
9 6713
3.2%
10 6808
3.3%
ValueCountFrequency (%)
31 3605
1.7%
30 6588
3.2%
29 6366
3.1%
28 6921
3.4%
27 6841
3.3%
26 6904
3.3%
25 6724
3.3%
24 7183
3.5%
23 6990
3.4%
22 6725
3.3%

day_of_week_account_created
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7621991
Minimum0
Maximum6
Zeros31830
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:56.573008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.944269
Coefficient of variation (CV)0.70388444
Kurtosis-1.1498462
Mean2.7621991
Median Absolute Deviation (MAD)2
Skewness0.16770178
Sum570651
Variance3.7801818
MonotonicityNot monotonic
2023-04-09T19:42:56.687455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 33992
16.5%
2 33040
16.0%
0 31830
15.4%
3 31499
15.2%
4 28811
13.9%
6 23733
11.5%
5 23688
11.5%
ValueCountFrequency (%)
0 31830
15.4%
1 33992
16.5%
2 33040
16.0%
3 31499
15.2%
4 28811
13.9%
5 23688
11.5%
6 23733
11.5%
ValueCountFrequency (%)
6 23733
11.5%
5 23688
11.5%
4 28811
13.9%
3 31499
15.2%
2 33040
16.0%
1 33992
16.5%
0 31830
15.4%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.37418
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-04-09T19:42:56.854527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.954891
Coefficient of variation (CV)0.5725276
Kurtosis-0.94130553
Mean24.37418
Median Absolute Deviation (MAD)11
Skewness0.25335698
Sum5035535
Variance194.73898
MonotonicityNot monotonic
2023-04-09T19:42:57.041571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 6777
 
3.3%
25 6426
 
3.1%
24 6164
 
3.0%
21 6116
 
3.0%
23 6061
 
2.9%
20 6057
 
2.9%
22 5606
 
2.7%
19 5486
 
2.7%
18 5441
 
2.6%
17 5308
 
2.6%
Other values (43) 147151
71.2%
ValueCountFrequency (%)
1 3198
1.5%
2 3826
1.9%
3 4025
1.9%
4 3785
1.8%
5 3795
1.8%
6 3913
1.9%
7 3844
1.9%
8 4038
2.0%
9 4282
2.1%
10 4236
2.1%
ValueCountFrequency (%)
53 3
 
< 0.1%
52 2671
1.3%
51 2785
1.3%
50 2891
1.4%
49 3173
1.5%
48 2806
1.4%
47 2893
1.4%
46 3063
1.5%
45 3068
1.5%
44 2745
1.3%

Interactions

2023-04-09T19:42:40.824780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:39.958751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:43.006368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:46.144052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:49.648709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:53.493724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:56.665986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:59.913536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:03.308758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:07.109982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:10.425473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:13.899058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:17.378102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:20.773939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:24.758459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:28.015642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:31.235624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:34.323099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:37.497901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:40.974493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:40.110070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:43.156317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:46.297560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:49.836310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:53.648732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:56.821568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:00.078977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:03.464438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:07.277725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:10.576976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:14.079258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:17.532646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:20.950526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:24.926603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:28.164307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:31.403414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:34.476271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:37.695689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:41.131414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:40.281971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:43.319969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:46.466632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:50.030966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:53.813357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:56.982393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:00.259002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:03.645738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:07.446947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:10.747417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:14.254579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:17.702195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:21.122574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:25.110073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:28.327425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:31.564345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:34.643447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:37.884662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:41.284511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:40.438147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:43.480243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:46.662924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-09T19:42:26.996833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:30.265011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:33.334616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:36.457398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:39.846991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:43.147349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:42.215689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:45.334302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:48.725541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:52.626580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:55.803922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:59.041507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:02.459540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:06.168531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:09.559161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:13.025686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:16.501481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:19.861067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:23.867774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:27.173327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:30.429569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:33.494230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:36.631053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:40.012833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:43.322220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:42.371713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:45.493466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:48.894349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:52.800179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:55.970613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:59.225558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:02.629961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:06.344556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:09.720715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:13.214676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:16.674170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:20.020038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:24.054476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:27.333056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:30.585690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:33.649220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:36.793478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:40.173562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:43.482050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:42.524313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:45.654606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:49.089168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:52.969824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:56.134744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:59.389709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:02.794729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:06.524158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:09.888802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:13.378969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:16.836800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:20.235003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:24.242072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:27.495821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:30.743145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:33.818165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:36.949074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:40.333701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:44.309928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:42.683588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:45.815966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:49.276570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:53.150164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:56.325258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:59.552451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:02.968252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:06.726042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:10.056731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:13.539893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:17.015379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:20.414530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:24.423463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:27.670062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:30.904756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:33.999956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:37.116333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:40.496395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:44.474090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:42.844263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:45.983541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:49.462472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:53.324501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:56.501341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:41:59.746591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:03.142163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:06.924790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:10.245249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:13.710652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:17.212456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:20.586367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:24.596366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:27.852156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:31.070832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:34.165677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:37.302478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T19:42:40.660762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-09T19:42:57.256652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
agesignup_flowdays_from_first_active_untill_bookingdays_from_first_active_untill_account_createddays_from_account_created_untill_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week__first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingmonth_account_createdday_account_createdday_of_week_account_createdweek_of_year_account_createdgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationyear_account_created
age1.000-0.0080.161-0.0020.1610.0040.009-0.0010.0200.0090.1870.0060.182-0.1840.0370.009-0.0010.0210.0090.3840.3080.0290.0520.0460.0430.0550.0490.0650.1030.063
signup_flow-0.0081.000-0.0130.004-0.013-0.006-0.0010.0110.034-0.0030.0070.0310.029-0.0280.031-0.0010.0110.034-0.0040.0980.2360.0140.3650.2670.1090.5690.2820.2580.0440.221
days_from_first_active_untill_booking0.161-0.0131.0000.0061.000-0.2420.083-0.006-0.0040.0800.8260.0500.718-0.7310.1680.083-0.006-0.0040.0800.1410.1020.0180.0950.0900.0480.1500.0980.0850.3150.598
days_from_first_active_untill_account_created-0.0020.0040.0061.000-0.013-0.0320.0050.000-0.0020.005-0.0190.006-0.0150.0170.0040.0050.0010.0000.0060.0220.0000.0000.0000.0020.0000.0030.0000.0030.0030.011
days_from_account_created_untill_first_booking0.161-0.0131.000-0.0131.000-0.2410.083-0.006-0.0040.0800.8270.0500.718-0.7310.1680.083-0.006-0.0040.0800.1570.0910.0180.0840.0930.0540.1610.0890.0860.3320.609
year_first_active0.004-0.006-0.242-0.032-0.2411.000-0.4950.0030.013-0.4810.265-0.2260.062-0.068-0.222-0.4950.0030.013-0.4810.1030.0860.0160.1120.1400.0600.1460.0960.1000.0460.998
month_first_active0.009-0.0010.0830.0050.083-0.4951.000-0.012-0.0110.975-0.0620.4400.009-0.0060.4170.999-0.012-0.0110.9750.0340.0540.0090.0660.0630.0370.0730.0630.0420.0170.307
day_first_active-0.0010.011-0.0060.000-0.0060.003-0.0121.000-0.0020.0570.012-0.0020.144-0.0050.005-0.0120.999-0.0020.0570.0040.0060.0000.0080.0170.0150.0170.0090.0090.0040.015
day_of_week__first_active0.0200.034-0.004-0.002-0.0040.013-0.011-0.0021.000-0.0150.002-0.004-0.0010.053-0.005-0.011-0.0020.999-0.0150.0030.0050.0060.0120.0130.0080.0300.0310.0300.0080.010
week_of_year_first_active0.009-0.0030.0800.0050.080-0.4810.9750.057-0.0151.000-0.0600.4390.018-0.0060.4220.9750.057-0.0150.9990.0310.0490.0100.0630.0620.0380.0670.0590.0390.0170.297
year_first_booking0.1870.0070.826-0.0190.8270.265-0.0620.0120.002-0.0601.000-0.0920.789-0.8060.037-0.0620.0120.002-0.0600.1260.0420.0190.0860.0990.0420.0860.0650.0720.4400.644
month_first_booking0.0060.0310.0500.0060.050-0.2260.440-0.002-0.0040.439-0.0921.0000.033-0.0380.9470.440-0.002-0.0040.4390.1150.0260.0120.0360.0250.0310.0550.0420.0400.3020.136
day_first_booking0.1820.0290.718-0.0150.7180.0620.0090.144-0.0010.0180.7890.0331.000-0.7190.1590.0080.144-0.0010.0180.1160.0230.0120.0330.0200.0230.0540.0400.0390.3120.044
day_of_week_first_booking-0.184-0.028-0.7310.017-0.731-0.068-0.006-0.0050.053-0.006-0.806-0.038-0.7191.000-0.154-0.006-0.0050.053-0.0060.1100.0210.0140.0330.0230.0210.0520.0440.0460.3600.043
week_of_year_first_booking0.0370.0310.1680.0040.168-0.2220.4170.005-0.0050.4220.0370.9470.159-0.1541.0000.4170.005-0.0050.4230.1110.0260.0120.0350.0240.0310.0530.0410.0390.2910.134
month_account_created0.009-0.0010.0830.0050.083-0.4950.999-0.012-0.0110.975-0.0620.4400.008-0.0060.4171.000-0.012-0.0110.9750.0340.0540.0090.0660.0630.0370.0730.0630.0420.0170.307
day_account_created-0.0010.011-0.0060.001-0.0060.003-0.0120.999-0.0020.0570.012-0.0020.144-0.0050.005-0.0121.000-0.0020.0570.0040.0060.0000.0080.0170.0150.0170.0090.0090.0040.015
day_of_week_account_created0.0210.034-0.0040.000-0.0040.013-0.011-0.0020.999-0.0150.002-0.004-0.0010.053-0.005-0.011-0.0021.000-0.0150.0030.0050.0060.0120.0130.0080.0300.0310.0300.0080.010
week_of_year_account_created0.009-0.0040.0800.0060.080-0.4810.9750.057-0.0150.999-0.0600.4390.018-0.0060.4230.9750.057-0.0151.0000.0310.0490.0100.0630.0620.0380.0680.0590.0390.0170.297
gender0.3840.0980.1410.0220.1570.1030.0340.0040.0030.0310.1260.1150.1160.1100.1110.0340.0040.0030.0311.0000.3770.0230.0650.0650.0350.0500.0640.0820.1260.103
signup_method0.3080.2360.1020.0000.0910.0860.0540.0060.0050.0490.0420.0260.0230.0210.0260.0540.0060.0050.0490.3771.0000.0450.1380.1210.0410.2230.1900.1190.0260.086
language0.0290.0140.0180.0000.0180.0160.0090.0000.0060.0100.0190.0120.0120.0140.0120.0090.0000.0060.0100.0230.0451.0000.0450.0770.0300.0190.0260.0420.0170.019
affiliate_channel0.0520.3650.0950.0000.0840.1120.0660.0080.0120.0630.0860.0360.0330.0330.0350.0660.0080.0120.0630.0650.1380.0451.0000.6210.3670.2860.1520.1370.0400.125
affiliate_provider0.0460.2670.0900.0020.0930.1400.0630.0170.0130.0620.0990.0250.0200.0230.0240.0630.0170.0130.0620.0650.1210.0770.6211.0000.3300.2310.1200.0830.0240.157
first_affiliate_tracked0.0430.1090.0480.0000.0540.0600.0370.0150.0080.0380.0420.0310.0230.0210.0310.0370.0150.0080.0380.0350.0410.0300.3670.3301.0000.1630.1230.1380.0260.067
signup_app0.0550.5690.1500.0030.1610.1460.0730.0170.0300.0670.0860.0550.0540.0520.0530.0730.0170.0300.0680.0500.2230.0190.2860.2310.1631.0000.6490.5030.0610.146
first_device_type0.0490.2820.0980.0000.0890.0960.0630.0090.0310.0590.0650.0420.0400.0440.0410.0630.0090.0310.0590.0640.1900.0260.1520.1200.1230.6491.0000.6310.0450.107
first_browser0.0650.2580.0850.0030.0860.1000.0420.0090.0300.0390.0720.0400.0390.0460.0390.0420.0090.0300.0390.0820.1190.0420.1370.0830.1380.5030.6311.0000.0380.113
country_destination0.1030.0440.3150.0030.3320.0460.0170.0040.0080.0170.4400.3020.3120.3600.2910.0170.0040.0080.0170.1260.0260.0170.0400.0240.0260.0610.0450.0381.0000.052
year_account_created0.0630.2210.5980.0110.6090.9980.3070.0150.0100.2970.6440.1360.0440.0430.1340.3070.0150.0100.2970.1030.0860.0190.1250.1570.0670.1460.1070.1130.0521.000

Missing values

2023-04-09T19:42:45.029321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T19:42:46.337872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idgenderagesignup_methodsignup_flowlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationdays_from_first_active_untill_bookingdays_from_first_active_untill_account_createddays_from_account_created_untill_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week__first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of_week_account_createdweek_of_year_account_created
0gxn3p5htnn-unknown-49facebook0endirectdirectuntrackedWebMac DesktopChromeNDF22934661827200931931220156290272010628026
1820tgsjxq7MALE38facebook0enseogoogleuntrackedWebMac DesktopChromeNDF22287321496200952352120156290272011525221
24ft3gnwmtxFEMALE56basic3endirectdirectuntrackedWebWindows DesktopIEUS419476-572009691242010820312010928139
3bjjt8pjhukFEMALE42facebook0endirectdirectuntrackedWebMac DesktopFirefoxother1043765278200910315442012985362011125049
487mebub9p4-unknown-41basic0endirectdirectuntrackedWebMac DesktopChromeUS72280-20820091281502010218372010914137
5osr2jwljor-unknown-49basic0enotherotheromgWebMac DesktopChromeUS101201011453201012553201011453
6lsw9q7uk0jFEMALE46basic0enothercraigslistuntrackedWebMac DesktopSafariUS30320101255320101511201012553
70d01nltbrsFEMALE47basic0endirectdirectomgWebMac DesktopSafariUS10010201013653201011322201013653
8a1vcnhxeijFEMALE50basic0enothercraigslistuntrackedWebMac DesktopSafariUS206020620101401201072933020101401
96uh8zyj2gn-unknown-46basic0enothercraigslistomgWebMac DesktopFirefoxUS000201014012010140120101401
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